Applications of Artificial Intelligence in Food Science and Technology Area (Turkish with English Abstract)

Abstract

Artificial intelligence (AI) is relatively new computational tools that have found extensive utilization in solving many complex real-world problems. AI has been utilized in a variety of applications ranging from modeling, classification, pattern recognition, and multivariate data analysis. Sample applications include numerical sign processing, control design, communication technologies; interpreting pyrolysis mass spectrometry, GC, and HPLC data, pattern recognition of DNA, RNA, protein structure, and microscopic images, prediction of microbial growth, biomass, and shelf life of food products, and identification of microorganisms and molecules. In food processing and engineering especially artificial neural networks, fuzzy logic and genetic algorithms techniques have been used to improve performance. Artificial intelligence techniques have been recently introduced as a tool for data analysis in food science and industry. AI has been used in food science and technology for classification, process modelling and optimization, quality control of foods prediction of dough rheological properties, classification of wine depending on anthocyanins content, prediction of the maximum or minimum temperature reached in the sample after pressurization and the time needed for thermal re-equilibration in the high-pressure food processing system, classification of fruits and vegetables according to their morphologic properties.

Keywords

• Artificial neural network, neural networks, fuzzy logic, genetic algorithm.  

Gıda Bilimi ve Teknolojisi Alanında Yapay Zekâ Uygulamaları

Abstract

Bilgi teknolojileri kullanma isteğiyle, araştırma ve uygulamalarda elde edilen bilginin toplanması, depolanması, sınıflanması, yönetimi ve kullanımını etkinleştirmek, kolaylaştırmak ve yaymak için araçlar, yöntemler geliştirmek ve kullanımını öğretmek amacında olan yapay zekâ (YZ) modelleme programları kullanılır hale gelmiştir. Bu programlar sayısal işaret işleme, kontrol tasarımı, haberleşmeden; GC, HPLC, kütle spektrofotometre datalarının analizi, RNA ve DNA tiplemesi, proteinlerin yapılarının tanımlanması, mikroskobik görüntülerin tanımlanması, biyokütle ve mikrobiyal gelişim tahminleri, gıdalarda raf ömrünün belirlenmesi, mikroorganizmaların tanımından molekül yapılarının belirlenmesine kadar birçok alanda kullanılabilmektedir. Gıda mühendisliğinde ise özellikle yapay sinir ağları (neural network), bulanık mantık (fuzzy logic) ve genetik algoritma (genetic algorithm) kullanılmaktadır. Gıdalarda ürün derecelendirme, sınıflandırma, proses modelleme ve optimizasyonu, kalite kontrolünün izlenmesi, görüntünün sayısal verilere dönüştürülmesi, ürün tasarımı, depolama sistemlerinin kontrolü, ürün rekoltesinin tahmini gibi alanlarda; endüstriyel ekmek mayası fermantasyonunda biokütle kestirimi, hamurun rheolojik özelliklerinin belirlenmesi, gıdalarda ısı prosesi değerlendirmesinde, görünür gözeneklilik, sıcaklık ve nem içeriğine göre ısı geçirgenliği tahmininde, antosiyonin içeriklerinin belirlenerek şarapların sınıflandırılması, meyve, sebze ve kuruyemişlerin morfolojik özelliklerine göre sınıflandırılması vb. modelleme uygulamaları yapılmıştır.

Keywords

• Yapay zekâ, yapay sinir ağları, bulanık mantık, genetik algoritma

References

1. Öztemel E. 2003. Yapay Sinir Ağları, Papatya Yayıncılık 232s, İstanbul.
2. Karakuzu C, Türker M, Öztürk S. 2002. Endüstriyel Ekmek Mayası Fermantasyonunda Yapay Sinir Ağı ile Biyokütle Kestirimi, 10. Sinyal İşleme Ve İletişim Uygulamaları Kurultayı (SIU’2002) Bildiriler Kitabı, s. 260-265, 12-14 Haziran 2002, Pamukkale/Denizli.
3. Liao K, Paulsen MR, Reid JF, Ni BC,Bonifacio-Maghirang EP. 1993. Corn kernel breakage classification by machine vision using a neural network classifier. Transactions of the ASAE, 36(6), 1949–1953.
4. Ruan R, Almaer S and Zhang J. 1995. Prediction of Dough Rheological Properties Using Neural Networks. Cereal Chem., 72(3): 308-311.
5. Hussian MA, Shafiur M, Rahman CW, Ng. 2002. Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network. Journal of Food Engineering, 51: 239-248.
6. Torrecilla JS, Otero L, Sanz PD. 2004. Artificial neural networks: a promising tool to design and optimize high-pressure food processes. Journal of Food Engineering, 62: 89–95.
7. Cimander C, Carlsson M, Mandenius CF. 2002. Sensor fusion for on-line monitoring of yoghurt fermentation. Journal of biotechnology. 99(3): 237 – 48.
8. Yongnian N, and Chao L.1999.Artificial neural networks and multivariate calibration for spectrophotometric differential kinetic determinations of food antioxidants. Analytica Chimica Acta 396: 221–230.

9. Mittal GS and Zhang J. 2000. Prediction of freezing time for food products using a neural network Food Research International, 33: 557-562.
10. Jeyamkondan S, Jayas DS and Holley RA.1999. Neural Networks For Modelling Microbial Growth 1999 North-Central Inter-Sectional Asae & Csae Conference. MBSK99-132.
11. Yu C, Davidson Valerie J, Simon X, Yang. 2005. A neural network approach to predict survival/death and growth/no- growth interfaces for Escherichia coli O157:H7. Food Microbiology.
12. Talon R, Walter D, Viallon C, Berdague JL. 2002. Prediction of Streptococcus salivarius subsp. thermophilus and Lactobacillus delbrueckii subsp. bulgaricus populations in yoghurt by Curie point pyrolysis-mass spectrometry. Journal of Microbiological Methods, 48: 271-279(9).
13. Sofu A. 2006. Yoğurtların depolama esnasında mikrobiyel ve kimyasal değişimlerinin bilgisayarlı görüntüleme sistemiyle belirlenmesi ve elde edilen verilerin yapay sinir ağlarıyla değerlendirilmesi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Gıda Mühendisliği Anabilim Dalı Yüksek Lisans Tezi, 98 s, Isparta.
14. Sofu A, Demir N, Perendeci A, ve Ekinci Kitiş FY. 2005. Yapay Sinir Ağlarının Gıda Endüstrisinde Kullanımı Gıda Kongresi 2005, İzmir.
15. Şen Z, 2004. Mühendislikte Bulanık (Fuzzy) Mantık İle Modelleme Prensipleri. Su Vakfı Yayıncılık 191s,. İstanbul.
16. Sofu A, and Ekinci Kitiş FY. 2005. Predicting survival rate of Yersinia Enterocolitica in Turkish feta cheese during maturation and storage by using fuzzy logic modelling. 1st International Food and Nutrition Congress - Food Safety, İstanbul.
17. Guillaume S, and Charnomordic B. 2000. Knowledge discovery for control purposes in food industry databases. Fuzzy Sets and Systems 122:487–497.
18. Rywotycki R. 2003. Food frying process control system. Journal of Food Engineering 59: 339–342.
19. Shahin MA, Tollner EW, and McClendon, RW. 2001. Artificial intelligence classifiers for sorting apples based on watercore. Journal of Agricultural Engineering Research, 79(3): 265–274.
20. Jahns G, Nielsen HM and Paul W. 2001. Measuring image analysis attributes and modelling fuzzy consumer aspects for tomato quality grading. Computers and Electronics in Agriculture, 31:17–29.
21. Ioannou I, Perrot N, Hossenlopp J, Mauris G, and Trystram G. 2002. The fuzzy set theory: a helpful tool for the estimation of sensory properties of crusting sausage appearance by a single expert. Food Quality and Preference, 13(7–8): 589–595.
22. Sun DW and Brosnan T. 2003a. Pizza quality evaluation using computer vision—Part 1 Pizza base and sauce spread. Journal of Food Engineering, 57: 81–89.
23. Sun DW and Brosnan T. 2003b. Pizza quality evaluation using computer vision—Part 2 Pizza topping analysis. Journal of Food Engineering, 57: 91–95.
24. Goldberg DE. 1989. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Mass.
25. Morimoto T, De Baerdemaeker J and Hashimoto Y. 1997. An intelligent approach for optimal control of fruit-storage process using neural networks and genetic algorithms. Computers and Electronics in Agriculture, 18: 205-224.
26. Ellis DI, Broadhurst D and Goodacre R. 2004. Rapid and quantitative detection of the microbial spoilage of beef by Fourier transform infrared spectroscopy and machine learning. Analytica Chimica Acta, 514: 193–201.
27. Chen CR and Ramaswamy HS. 2002. Modeling and optimization of variable retort temperature (VRT) thermal processing using coupled neural networks and genetic algorithms Journal of Food Engineering, 53: 09–220.
28. Doganis P, Alexandridis A, Patrinos P and Sarimveis H. 2006. Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. Journal of Food Engineering, 75: 196–20.
29. Gimeno R, Martı´nez C and Silo´niz M. 2002. Improving artificial neural networks with a pruning methodology and genetic algorithms for their application in microbial growth prediction in food. International Journal of Food Microbiology, 72: 19– 30.
30. Chen M, Chen K and Lin C. 2005. Optimization on response surface models for the optimal manufacturing conditions of dairy tofu. Journal of Food Engineering, 68: 471–480.
31. Torres M, Hervás C and Amador F. 2005. Approximating the sheep milk production curve through the use of artificial neural networks andgenetic algorithms Computers & Operations Research, 32: 2653–2670.
32. Barile D, Coısson JD, Arlorio M and Rinaldi M. 2006. Identification of production area of Ossolano Italian cheese with chemometric complex approach. Food Control, 17:197–206.
33. Morimoto T, Suzuki J and Hashimoto Y. 1997. Optimization of a Fuzzy Controller for Fruit Storage Using Neural Networks and Genetic Algorithms. EngngApplic. Artif lntell., 10: 453-461.
34. Morimoto T, Purwanto W, Suzuki J and Hashimoto Y. 1997. Optimization of heat treatment for fruit during storage using neural networks and genetic algorithms. Computers and Electronics in Agriculture, 19: 87–101.
35. Izadifar M, Jahromi Mz. 2007. Application of genetic algorithm for optimization of vegetable oil hydrogenation process. Journal of Food Engineering, 78:1-8.
36. Dutta J, Dutta P and Banerjee R. 2005. Modeling and optimization of protease production by a newly isolated Pseudomonas sp. using a genetic algorithm. Process Biochemistry, 40: 879–884.
37. Llet´ı R, Mel´endez E, Ortiz MC, Sarabia LA, and S´anchez MS.2005. Outliers in partial least squares regression Application to calibration of wine grade with mean infrared data. Analytica Chimica Acta, 544: 60–70.
38. Buratti S, Ballabio D, Benedetti S and Cosio M.S. 2007. Prediction of Italian red wine sensorial descriptors from electronic nose, electronic tongue and spectrophotometric measurements by means of Genetic Algorithm regression models. Food Chemistry, 100:211-218.